Biology subjects

Pitt, J.

Publications and source records attributed to Pitt, J..

3 recordsLinked to original sources

bioRxiv2024

Effects of age on resting-state cortical networks

Understanding how ageing affects brain function remains a central challenge in neuroscience. Electrophysiological brain imaging techniques provide a near-direct measure of neuronal activity, which is useful for characterising neurophysiological health. They offer us the ability to track large-scale networks of functional activity with high temporal precision. The effects of healthy ageing on these networks remain poorly understood, in part due to small sample sizes and limited control for confounding factors in previous studies. Here, we analysed resting-state source-reconstructed magnetoencephalography (MEG) data from a large cross-sectional cohort of healthy adults (N =612, 18-88 years old) to characterise the effect of age using not only time-averaged (static), but also transient (dynamic) network activity. We examined time-averaged power and coherence across canonical frequency bands ({delta}, {theta}, , {beta}, {gamma}), as well as transient network dynamics identified using Hidden Markov Modelling. We included many confounding variables known to be affected by age, such as brain volume, as well as head size and position, which have previously been overlooked. Ageing was associated with frequency-specific changes in oscillatory power, with decreases in low-frequency ({delta}, {theta}) power and increases in high-frequency ({beta}) power. Coherence increased across all frequency bands and was positively associated with cognitive performance. Transient network analyses additionally revealed that frontal network occurrences declined with age, with evidence suggesting a compensatory role in supporting cognition. These findings provide a more comprehensive electrophysiological signature for healthy ageing and establish a baseline for detecting pathological change. HighlightsO_LIUsing source reconstructed magnetoencephalography data, we studied cortical functional networks of oscillatory activity from a large healthy population (612 subjects, 18-88 years old). C_LIO_LITime-averaged (static) network analysis in canonical frequency bands ({delta}, {theta}, , {beta}, {gamma}) showed: {beta} power and coherence increased with age; {delta}/{theta} power decreased with age, but coherence increased. Coherence in all frequencies increased with cognitive performance. C_LIO_LITransient (dynamic) network analysis showed frontal networks decreased in occurrence with age and their relation to cognitive performance showed that these changes were compensatory. The other networks showed a decreased occurrence with age. C_LIO_LIIn testing age and cognitive performance effects for statistical significance, we included a comprehensive set of confounds, such as brain volumes, head size and position, which has previously been overlooked. C_LIO_LIWe provide a comprehensive electrophysiological signature for healthy ageing and establish a baseline for detecting pathological change. C_LI

neuroscience↗

bioRxiv2023

Reliability of dynamic causal modelling of resting state magnetoencephalography

This study assesses the reliability of resting-state dynamic causal modelling (DCM) of magneto-electroencephalography under conductance-based canonical microcircuit models, in terms of both posterior parameter estimates and model evidence. We use resting state magneto-electroencephalography (MEG) data from two sessions, acquired two weeks apart, from a cohort with high between-subject variance arising from Alzheimers disease. Our focus is not on the effect of disease, but on the predictive validity of the methods implicit in their reliability, which is crucial for future studies of disease progression and drug intervention. To assess the predictive validity of first-level DCMs, we compare model evidence associated with the covariance among subject-specific free energies (i.e., the quality of the models) with vs. without interclass correlations. We then used parametric empirical Bayes (PEB) to investigate the predictive validity of DCM parameters at the between subject level. Specifically, we examined the evidence for or against parameter differences (i) within-subject, within-session, between-epochs; (ii) within-subject between-session and (iii) within-site between-subjects, accommodating the conditional dependency among parameter estimates. We show that for data acquired close in time, and under similar circumstances, more than 95% of inferred DCM parameters are unlikely to differ, speaking to mutual predictability over sessions. Using PEB, we show a reciprocal relationship between a conventional definition of reliability and the conditional dependency among inferred model parameters. Our analyses confirm the predictive validity and reliability of the conductance-based DCMs for resting-state neurophysiological data. In this respect, the implicit generative modelling is suitable for interventional and longitudinal studies of neurological and psychiatric disorders. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=147 SRC="FIGDIR/small/562379v1_ufig1.gif" ALT="Figure 1"> View larger version (41K): org.highwire.dtl.DTLVardef@135c964org.highwire.dtl.DTLVardef@38e4a7org.highwire.dtl.DTLVardef@12bc358org.highwire.dtl.DTLVardef@25f110_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

bioRxiv2022

The GLM-Spectrum: A multilevel framework for spectrum analysis with covariate and confound modelling

The frequency spectrum is a central method for representing the dynamics within electrophysiological data. Some widely used spectrum estimators make use of averaging across time segments to reduce noise in the final spectrum. The core of this approach has not changed substantially since the 1960s, though many advances in the field of regression modelling and statistics have been made during this time. Here, we propose a new approach, the General Linear Model (GLM) Spectrum, which reframes time averaged spectral estimation as multiple regression. This brings several benefits, including the ability to do confound modelling, hierarchical modelling and significance testing via non-parametric statistics. We apply the approach to a dataset of EEG recordings of participants who alternate between eyes-open and eyes-closed resting state. The GLM-Spectrum can model both conditions, quantify their differences, and perform denoising through confound regression in a single step. This application is scaled up from a single channel to a whole head recording and, finally, applied to quantify age differences across a large group-level dataset. We show that the GLM-Spectrum lends itself to rigorous modelling of within- and between-subject contrasts as well as their interactions, and that the use of model-projected spectra provides an intuitive visualisation. The GLM-Spectrum is a flexible framework for robust multi-level analysis of power spectra, with adaptive covariance and confound modelling.

neuroscience↗